Symptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods
| dc.authorid | 0000-0001-6574-8149 | |
| dc.authorid | 0000-0002-4574-421X | |
| dc.authorid | 0000-0002-0220-1207 | |
| dc.authorid | 0000-0001-9881-6013 | |
| dc.authorid | 0000-0002-0578-3126 | |
| dc.contributor.author | Bolat, Hilmi | |
| dc.contributor.author | Bulut, Edanur | |
| dc.contributor.author | Ünsel-bolat, Gül | |
| dc.contributor.author | Özgül, Semiha | |
| dc.contributor.author | Turan, Duygu Selin | |
| dc.contributor.author | Çeli̇k, Samet | |
| dc.contributor.author | Koyuncu, Özgür Ozan | |
| dc.date.accessioned | 2026-06-23T06:27:02Z | |
| dc.date.issued | 2026 | |
| dc.department | Fakülteler, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü | |
| dc.description | Bolat, Hilmi - Bulut Edanur (Balikesir Author) | |
| dc.description.abstract | Purpose: Copy number variations (CNVs) in the 16p11.2 region are well-established contributors to neurodevelopmental disorders, yet phenotype variability across this locus remains insuffi ciently characterized. This study investigates clinical features and ASD-related symptoms among carriers of rare pathogenic and common CNVs, and evaluates symptom-level discriminability using machine learning (ML) methods. Methods: Genetic data from 7568 individuals were retrospectively screened, identifying 147 carriers of 16p11.2 CNVs. Detailed clinical assessments were completed for 50 participants. ASDrelated symptoms were evaluated using a structured 25-item instrument. Group comparisons applied nonparametric statistics with effect sizes, confidence intervals, and FDR correction. ML analyses used PCA and k-means for feature selection, oversampling methods (SMOTE, BorderlineSMOTE, ADASYN), and five classifiers, evaluated through cross-validation. Results: Across pathogenic and common CNV groups, no significant differences were observed in social communication, restricted/repetitive behaviors, sensory symptoms, regression, or total autism scores (FDR-adjusted p > 0.05). Aggression was more frequently endorsed in pathogenic CNV carriers (raw p = 0.030; FDR p = 0.098). BMI was higher in pathogenic CNVs, though nonsignificant after correction (raw p = 0.027; FDR p = 0.152). ML analyses identified three recurrent discriminative symptoms across multiple datasets: delayed response to name, unusual object play, and aggression. Dataset 3 (16 symptoms) provided the most balanced classification performance but, given the very small pathogenic CNV sample, results remain exploratory. Conclusion: Findings suggest that, while most autism-related symptoms do not differ between groups, aggression and increased BMI may represent preliminary phenotypic signals associated with pathogenic CNVs. Integrating clinical data from 147 CNV carriers further supports a po tential widespread effect across the broader 16p11.2 locus rather than a single breakpoint-specific mechanism. However, all results should be interpreted cautiously due to limited sample size, and larger, systematically phenotyped cohorts are required to establish robust genotype–phenotype relationships. | |
| dc.description.sponsorship | Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) 321S239 | |
| dc.identifier.doi | 10.1016/j.reia.2026.202865 | |
| dc.identifier.endpage | 12 | |
| dc.identifier.issn | 3050-6573 | |
| dc.identifier.scopus | 2-s2.0-105030284433 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.startpage | 1 | |
| dc.identifier.uri | https://doi.org/10.1016/j.reia.2026.202865 | |
| dc.identifier.uri | 3050-6565 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12462/24092 | |
| dc.identifier.volume | 132 | |
| dc.identifier.wos | WOS:001700449100001 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Elsevier Ltd | |
| dc.relation.ispartof | Research in Autism | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.relation.tubitak | 321S239 | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | 16p11.2 | |
| dc.subject | CNVs | |
| dc.subject | Machine Learning | |
| dc.subject | Neurodevelopmental Disorders | |
| dc.title | Symptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods | |
| dc.type | Article |












